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Nomogram Model for Predicting the Risk of Multidrug-Resistant Bacteria Infection in Diabetic Foot Patients

Authors Ma Y, Zhang L, Hu Y, Shi T

Received 28 October 2020

Accepted for publication 13 January 2021

Published 18 February 2021 Volume 2021:14 Pages 627—637

DOI https://doi.org/10.2147/IDR.S287852

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

Editor who approved publication: Professor Suresh Antony


Yi-ni Ma,1,* Li-xiang Zhang,2,* Yuan-yuan Hu,3 Tian-lu Shi1

1Department of Pharmacy, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230036, People’s Republic of China; 2Department of Cardiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230036, People’s Republic of China; 3Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230036, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Tian-lu Shi
Department of Pharmacy, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230036, People’s Republic of China
Tel +86-18900518553
Email ahslyy4224@163.com

Objective: This study established an individualized nomogram for predicting the risk of multidrug-resistant bacterial (MDRB) infection in patients with the diabetic foot (DF), and providing a reference for clinical prevention and treatment.
Methods: A total of 199 DF patients admitted to the hospital from July 2015 to December 2018 were included in this study. The pathogenic bacteria at the site of infection were detected and the factors affecting the occurrence of MDRB infection in DF patients summarized. The R software was used to draw the nomogram, and the Bootstrap Method used to internally verify the model. The calibration curve and the Harrell’s Concordance Index (C-index) were used to evaluate the predictive effect of the nomogram model.
Results: Logistic regression analysis showed that age, course of diabetes, previous use of antibacterial drugs, types of antibacterial drugs, and osteoporosis were risk factors for multidrug-resistant infections in DF (P< 0.05). The area under the receiver operating characteristic curve (AUC, Area Under Curve) of the nomogram model after internal verification was 0.773 (95% CI: 0.704– 0.830). The mean absolute error between the predicted probability of infection in the nomogram and the actual occurrence of MDRB was 0.032, indicating that the nomogram model had good forecasting efficiency and stability.
Conclusion: The risk factors for multidrug-resistant infections in DF are age, course of diabetes, previous use of antibacterial drugs, types of antibacterial drugs used, and osteoporosis. The nomogram model drawn on these risk factors has good predictive accuracy and can assist medical staff in formulating targeted infection prevention strategies for patients.

Keywords: diabetic foot, multidrug-resistant bacteria, infection, risk-factors, nomogram

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